Who Do “Expert” or “Influencer” Lists & Surveys Really Benefit And Are They Useful?

You might have noticed the number of “influencer” lists and “expert” surveys appearing from time-to-time on LinkedIn. And given my training in accounting I was perturbed to see that these were increasingly infiltrating LinkedIn and our profession which has various standards around objectivity, rationality and casting a critical eye. So I recently conducted an experiment to better understand why are they so popular? and what value do they hold? And for whom? The findings were unexpected. Here is Part 1.

For those curious to see the list which I did in a common perceptual map form (more on that later as to why) here is the link (I am not tracking or commenting on this one anymore): https://www.dhirubhai.net/feed/update/urn:li:activity:6575309489593954304/

Reasons for the experiment

On LinkedIn I’m delighted to see more content being shared and created by practitioners and service providers in our profession. Compared to 3 to 4 years ago when I started interacting more than simply casually on the platform we’re much more engaged with on the whole some very good content and opinions being shared. This is a good thing. A not so good thing is when “expert” lists are being wrapped up as objective, or there’s a survey that’s been posted by an influencer with strong links to say a financial software vendor that has misrepresented the results in favour of shining a positive light on the features and benefits of the vendor’s product.

And the expert list bug even bit a good friend of mine, you might have come across him, Anders who recently published a #financemaster list of 100 or so names in Finance (by the way I actually like the effort Anders goes to in this list and it’s very helpful to identify content producers, but we all have our opinions, my role here is to get some objective data on them).

And let’s say we park to one side the design of such surveys and lists, who at the end of the day really benefits from them? The content creator? The content consumer? Those post of the list or survey? Before I get onto these questions I’d like to address why I chose a perceptual map approach instead of a numbered list.

Going for Impact

The reason for this perceptual map approach is based around the quality of impact it has on the human mind. Numbered lists and tables have a couple inherent flaws, there is one, a lot of detail and so they’re not as easily consumed; and two, numbers are a relatively new invention for us humans, the current numbering system we are familiar with was codified about 1,300 years ago, whereas images and pictures have been around many tens of millennia more. Additionally, because we are bombarded with millions of bits of information every day our brains have simplified the process of paying attention to the most relevant ones by developing selective filters, formed part consciously and part subconsciously, which is sometimes referred to as unconscious bias.

Also one of the most well known influencer-type list is the infamous Gartner Magic Quadrant, so why not borrow from the best (known) at it.

The thing is that such perceptual maps are generally subjective, their scoring systems are opaque and heavily biased. Far from playing up to the standards of objectivity required of accountants & finance professionals, instead their design and ease of use to be interpreted by our subconscious minds makes them potentially able to bypass most of our objectivity sensors and heavily influence our behaviour in certain ways.

Experiment Design

So I took Anders’s list from which I copied 40 names, ordered them in line with my selective own biases using my own subjective scoring system what some might call, “an opinion” to arrive at the #sitn influencer 4-Box. It doesn’t make my selections and positioning right or wrong, they are just an opinion of my own making. (Note: this process I’m consciously talking about is how I and most people genuinely position others in my minds at a subconscious level if we care to reflect more on it. Coming back to the experiment there are some on this list whom I follow more than others, but even if I did follow all to the same degree there’s no way I could have full visibility of all their impacts, so this opinion is also subject to change day to day and over time).

The remaining 60 less 1 (me the poster), 59, would be the control group to assess the 40 influencers against.

Bringing objectivity to “expert” lists

The Post & Poster over the 7 days:

The post generated 19,515 views; 152 reactions; 150 comments; and 16 reshares.

This is 5X the attention a usual #SITN rated post would get.

The associated article explaining the list garnered 234 views, 33 reactions & 9 comments. This is about average or no change on the usual levels.

The #SITN company page followers increased by 142 to 342 (a 105% increase over the prior week)

My profile views were 236 during the week, which is about normal for me. No change there.

The number of followers went up 130 to 4,080 (a 3.3% increase)

For the Influencers over the 7 days:

See the following table of summary indicators

As well as the box & whisker plot

On average being on #SITN Finance Influencer 4-Box had no real benefit for those in it. In fact the largest week over week follower gains were in the Control group. Even the distributions were broadly the same.

In fact let’s say for two seconds that when I put the 4-Box together that I somehow possessed expert quality, a bit like a soccer pundit predicting the results of the weekend’s soccer games,

it actually turns out that the success of my expert rating power is worse than that of the control group.

By the way I’m not surprised by this because whilst I have my own subjective map, I don’t believe I possess any expert qualities other than maybe asking questions of podcasts guests and of such expert lists. Two other bits of evidence support this outcome: 1. That’s why mentors are invited onto the #SITN show, because I don’t have all the answers; and 2. Studies on the accuracy of expert predictions, particularly Tetlock’s, suggest experts struggled to perform better than “dart-throwing chimps”, and were consistently less accurate than even relatively simple statistical algorithms.

Interestingly one person in the control group actually had their number of followers contract, hence the negative minimum score.

However when you compare to those results of the poster you see that generating such subjective lists or in our experiment’s case, the matrix, is more than 2X more effective for the poster than those generating the content and making an impact. So in terms of cold hard numbers this would suggest that so-called experts or posters of such lists are the main beneficiaries and really aren’t the ones who should be getting the following or credit, instead it should be going to it’s those who are really doing something via their hard work and efforts.

Sentiment Analysis

So the last statistical analysis I have done has been around sentiment, i.e. overall was the list received, positively, negatively or was it more neutral. Rightly or wrongly I took the comments (rather than likes) as proxy votes on sentiment and read through all 159 of them (150 from the post; 9 from the article).

Note: I have to exclude any comments I made back to keep readers engaged in the conversation. Also anyone who challenged the objectivity of the list has been included in the negative category, and if the comment was constructive but not really positive, went at a tangent or recommended a person missing in the list this is counted as neutral.  

In summary, when analysing the comments of the 4-Box it seems to have been received positively by the majority of them however thankfully there were a few who did question its usefulness as well as the veracity of the scoring system. Also when reading a number of the positive comments, there was frequent reference to liking the layout design and “graphical” nature of it (this should not be a surprise as human brains like it when we require less energy to consume information)

But what these numbers miss!

However, sentiment analysis alone misses out on a few key points. We’re a profession that I’d like to believe prides ourselves on objectivity, accurate measurement and rationality. Interestingly there were some surprising behaviours that also followed on from this list during the week after publication which as an accounting & finance community I’d like to bring forward for a more general and measured debate. So I close this chapter on Part 1 of my analysis, and Part 2 which for now I’m calling the Good, the Bad & the Ugly will provide a more behavioural as opposed to statistical analysis of the experiment.

Part 1 key points:

• These expert lists & surveys are being made easy to consume and share hence their popularity (and also why I believe if done in the right way, they have an appropriate place, more on that next week).
• The poster and their organisation are the main beneficiary of expert lists, there is not much benefit for those named as content providers and influencers in such posts, which is ironic since they are doing most of the work.
• They are generally seen as positive with very few people casting an objective eye over them.

Note: What was under the microscope was my own subjective perceptual map (i.e. my opinion). To be clear, I genuinely follow, consume and appreciate the content of those producers who I had as influencers. And it’s just my opinion, they are all leading lights in our accounting & finance community no matter what category my mind perceives them in.

Call to action: What do you think? Are these expert influencer list & surveys of much benefit, and to whom? Does it matter? And who cares? Leave your comments so we can ensure better more objective lists and surveys are developed by posters.

About the Author

The author Andrew Codd is the producer of the #SITN Podcast which has a finance & accounting audience in over 150 countries worldwide. Each week #SITN interviews real practitioners to break down their hard won lessons and deconstruct their practical methods that work on the job and which you won't typically find in textbooks or exams so that we create more influential finance professionals worldwide who solve meaningful problems for their organisations and in return have fun, rewarding and successful careers in finance